3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS

Face recognition has attracted many researchers’ attention compared to other biometrics due to its non-intrusive and friendly nature. Although several methods for 2D face recognition have been proposed so far, there are still some challenges related to the 2D face including illumination, pose variation, and facial expression. In the last few decades, 3D face research area has become more interesting since shape and geometry information are used to handle challenges from 2D faces. Existing algorithms for face recognition are divided into three different categories: holistic feature-based, local feature-based, and hybrid methods. According to the literature, local features have shown better performance relative to holistic feature-based methods under expression and occlusion challenges. In this dissertation, local feature-based methods for 3D face recognition have been studied and surveyed. In the survey, local methods are classified into three broad categories which consist of keypoint-based, curve-based, and local surface-based methods. Inspired by keypoint-based methods which are effective to handle partial occlusion, structural context descriptor on pyramidal shape maps and texture image has been proposed in a multimodal scheme. Score-level fusion is used to combine keypoints’ matching score in both texture and shape modalities. The survey shows local surface-based methods are efficient to handle facial expression. Accordingly, a local derivative pattern is introduced to extract distinct features from depth map in this work. In addition, the local derivative pattern is applied on surface normals. Most 3D face recognition algorithms are focused to utilize the depth information to detect and extract features. Compared to depth maps, surface normals of each point can determine the facial surface orientation, which provides an efficient facial surface representation to extract distinct features for recognition task. An Extreme Learning Machine (ELM)-based auto-encoder is used to make the feature space more discriminative. Expression and occlusion robust analysis using the information from the normal maps are investigated by dividing the facial region into patches. A novel hybrid classifier is proposed to combine Sparse Representation Classifier (SRC) and ELM classifier in a weighted scheme. The proposed algorithms have been evaluated on four widely used 3D face databases; FRGC, Bosphorus, Bu-3DFE, and 3D-TEC. The experimental results illustrate the effectiveness of the proposed approaches. The main contribution of this work lies in identification and analysis of effective local features and a classification method for improving 3D face recognition performance

Hybrid 2D and 3D face verification

Face verification is a challenging pattern recognition problem. The face is a biometric that, we as humans, know can be recognised. However, the face is highly deformable and its appearance alters significantly when the pose, illumination or expression changes. These changes in appearance are most notable for texture images, or two-dimensional (2D) data. But the underlying structure of the face, or three dimensional (3D) data, is not changed by pose or illumination variations. Over the past five years methods have been investigated to combine 2D and 3D face data to improve the accuracy and robustness of face verification. Much of this research has examined the fusion of a 2D verification system and a 3D verification system, known as multi-modal classifier score fusion. These verification systems usually compare two feature vectors (two image representations), a and b, using distance or angular-based similarity measures. However, this does not provide the most complete description of the features being compared as the distances describe at best the covariance of the data, or the second order statistics (for instance Mahalanobis based measures). A more complete description would be obtained by describing the distribution of the feature vectors. However, feature distribution modelling is rarely applied to face verification because a large number of observations is required to train the models. This amount of data is usually unavailable and so this research examines two methods for overcoming this data limitation: 1. the use of holistic difference vectors of the face, and 2. by dividing the 3D face into Free-Parts. The permutations of the holistic difference vectors is formed so that more observations are obtained from a set of holistic features. On the other hand, by dividing the face into parts and considering each part separately many observations are obtained from each face image; this approach is referred to as the Free-Parts approach. The extra observations from both these techniques are used to perform holistic feature distribution modelling and Free-Parts feature distribution modelling respectively. It is shown that the feature distribution modelling of these features leads to an improved 3D face verification system and an effective 2D face verification system. Using these two feature distribution techniques classifier score fusion is then examined. This thesis also examines methods for performing classifier fusion score fusion. Classifier score fusion attempts to combine complementary information from multiple classifiers. This complementary information can be obtained in two ways: by using different algorithms (multi-algorithm fusion) to represent the same face data for instance the 2D face data or by capturing the face data with different sensors (multimodal fusion) for instance capturing 2D and 3D face data. Multi-algorithm fusion is approached as combining verification systems that use holistic features and local features (Free-Parts) and multi-modal fusion examines the combination of 2D and 3D face data using all of the investigated techniques. The results of the fusion experiments show that multi-modal fusion leads to a consistent improvement in performance. This is attributed to the fact that the data being fused is collected by two different sensors, a camera and a laser scanner. In deriving the multi-algorithm and multi-modal algorithms a consistent framework for fusion was developed. The consistent fusion framework, developed from the multi-algorithm and multimodal experiments, is used to combine multiple algorithms across multiple modalities. This fusion method, referred to as hybrid fusion, is shown to provide improved performance over either fusion system on its own. The experiments show that the final hybrid face verification system reduces the False Rejection Rate from 8:59% for the best 2D verification system and 4:48% for the best 3D verification system to 0:59% for the hybrid verification system; at a False Acceptance Rate of 0:1%

3d Face Reconstruction And Emotion Analytics With Part-Based Morphable Models

3D face reconstruction and facial expression analytics using 3D facial data are new and hot research topics in computer graphics and computer vision. In this proposal, we first review the background knowledge for emotion analytics using 3D morphable face model, including geometry feature-based methods, statistic model-based methods and more advanced deep learning-bade methods. Then, we introduce a novel 3D face modeling and reconstruction solution that robustly and accurately acquires 3D face models from a couple of images captured by a single smartphone camera. Two selfie photos of a subject taken from the front and side are used to guide our Non-Negative Matrix Factorization (NMF) induced part-based face model to iteratively reconstruct an initial 3D face of the subject. Then, an iterative detail updating method is applied to the initial generated 3D face to reconstruct facial details through optimizing lighting parameters and local depths. Our iterative 3D face reconstruction method permits fully automatic registration of a part-based face representation to the acquired face data and the detailed 2D/3D features to build a high-quality 3D face model. The NMF part-based face representation learned from a 3D face database facilitates effective global and adaptive local detail data fitting alternatively. Our system is flexible and it allows users to conduct the capture in any uncontrolled environment. We demonstrate the capability of our method by allowing users to capture and reconstruct their 3D faces by themselves. Based on the 3D face model reconstruction, we can analyze the facial expression and the related emotion in 3D space. We present a novel approach to analyze the facial expressions from images and a quantitative information visualization scheme for exploring this type of visual data. From the reconstructed result using NMF part-based morphable 3D face model, basis parameters and a displacement map are extracted as features for facial emotion analysis and visualization. Based upon the features, two Support Vector Regressions (SVRs) are trained to determine the fuzzy Valence-Arousal (VA) values to quantify the emotions. The continuously changing emotion status can be intuitively analyzed by visualizing the VA values in VA-space. Our emotion analysis and visualization system, based on 3D NMF morphable face model, detects expressions robustly from various head poses, face sizes and lighting conditions, and is fully automatic to compute the VA values from images or a sequence of video with various facial expressions. To evaluate our novel method, we test our system on publicly available databases and evaluate the emotion analysis and visualization results. We also apply our method to quantifying emotion changes during motivational interviews. These experiments and applications demonstrate effectiveness and accuracy of our method. In order to improve the expression recognition accuracy, we present a facial expression recognition approach with 3D Mesh Convolutional Neural Network (3DMCNN) and a visual analytics guided 3DMCNN design and optimization scheme. The geometric properties of the surface is computed using the 3D face model of a subject with facial expressions. Instead of using regular Convolutional Neural Network (CNN) to learn intensities of the facial images, we convolve the geometric properties on the surface of the 3D model using 3DMCNN. We design a geodesic distance-based convolution method to overcome the difficulties raised from the irregular sampling of the face surface mesh. We further present an interactive visual analytics for the purpose of designing and modifying the networks to analyze the learned features and cluster similar nodes in 3DMCNN. By removing low activity nodes in the network, the performance of the network is greatly improved. We compare our method with the regular CNN-based method by interactively visualizing each layer of the networks and analyze the effectiveness of our method by studying representative cases. Testing on public datasets, our method achieves a higher recognition accuracy than traditional image-based CNN and other 3D CNNs. The presented framework, including 3DMCNN and interactive visual analytics of the CNN, can be extended to other applications

Evaluation and Applying Feature Extraction Techniques for Face Detection and Recognition

Detecting the image and identifying the face has become important in the field of computer vision for recognizing and analyzing, reconstructing into 3D, and labelling the image. Feature extraction is usually the first stage in detection and recognition of the image processing and computer vision. It supports the conversion of the image into a quantitative data. Later, this converted data can be used for labelling, classifying and recognizing a model. In this paper, performance of such feature extraction techniques viz. Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN) technique is applied to detect and recognize the face. The experiments conducted with a data set addressing the issues like pose variation, facial expression and intensity of light. The efficiency of the algorithms were evaluated based on the computational time and accuracy rate

Face recognition using local geometrical features - PCA with Euclidean classifier

The goal of this research is to get the minimum features and produce better recognition rates. Before doing the feature selection, we investigate automatic methods for detecting face anchor points with 412 3D-facial points of 60 individuals. There are 7 images per subject including views presenting light rotations and facial expressions. Each images have twelve anchor points which are Right Outer Eye, Right Inner Eye, Left Outer Eye, Left Inner Eye, Upper nose point, Nose Tip, Right Nose Base, Left Nose Base, Right Outer Face, Left Outer Face, Chin, and Upper Face. All the control points are based on the measurement on an absolute scale (mm). After all the control points have been determined, we will extract a relevant set of features. These features are classified in 3 : (1) distance of mass points, (2) angle measurements, and (3) angle measurements. There are fifty-three local geometrical features extracted from 3D points human faces to model the face for face recognition and the discriminating power calculation is to show the valuable feature among all the features. Experiment performed on the GavabDB dataset (412 faces) show that our algorithm achieved 86% of success when respectively the first rank matched

3D face structure extraction from images at arbitrary poses and under arbitrary illumination conditions

With the advent of 9/11, face detection and recognition is becoming an important tool to be used for securing homeland safety against potential terrorist attacks by tracking and identifying suspects who might be trying to indulge in such activities. It is also a technology that has proven its usefulness for law enforcement agencies by helping identifying or narrowing down a possible suspect from surveillance tape on the crime scene, or quickly by finding a suspect based on description from witnesses.In this thesis we introduce several improvements to morphable model based algorithms and make use of the 3D face structures extracted from multiple images to conduct illumination analysis and face recognition experiments. We present an enhanced Active Appearance Model (AAM), which possesses several sub-models that are independently updated to introduce more model flexibility to achieve better feature localization. Most appearance based models suffer from the unpredictability of facial background, which might result in a bad boundary extraction. To overcome this problem we propose a local projection models that accurately locates face boundary landmarks. We also introduce a novel and unbiased cost function that casts the face alignment as an optimization problem, where shape constraints obtained from direct motion estimation are incorporated to achieve a much higher convergence rate and more accurate alignment. Viewing angles are roughly categorized to four different poses, and the customized view-based AAMs align face images in different specific pose categories. We also attempt at obtaining individual 3D face structures by morphing a 3D generic face model to fit the individual faces. Face contour is dynamically generated so that the morphed face looks realistic. To overcome the correspondence problem between facial feature points on the generic and the individual face, we use an approach based on distance maps. With the extracted 3D face structure we study the illumination effects on the appearance based on the spherical harmonic illumination analysis. By normalizing the illumination conditions on different facial images, we extract a global illumination-invariant texture map, which jointly with the extracted 3D face structure in the form of cubic morphing parameters completely encode an individual face, and allow for the generation of images at arbitrary pose and under arbitrary illumination.Face recognition is conducted based on the face shape matching error, texture error and illumination-normalized texture error. Experiments show that a higher face recognition rate is achieved by compensating for illumination effects. Furthermore, it is observed that the fusion of shape and texture information result in a better performance than using either shape or texture information individually.Ph.D., Electrical Engineering -- Drexel University, 200

Feature extraction for range image interpretation using local topology statistics

This thesis presents an approach for interpreting range images of known subject matter, such as the human face, based on the extraction and matching of local features from the images. In recent years, approaches to interpret two-dimensional (2D) images based on local feature extraction have advanced greatly, for example, systems such as Scale Invariant Feature Transform (SIFT) can detect and describe the local features in the 2D images effectively. With the aid of rapidly advancing three-dimensional (3D) imaging technology, in particular, the advent of commercially available surface scanning systems based on photogrammetry, image representation has been able to extend into the third dimension. Moreover, range images confer a number of advantages over conventional 2D images, for instance, the properties of being invariant to lighting, pose and viewpoint changes. As a result, an attempt has been made in this work to establish how best to represent the local range surface with a feature descriptor, thereby developing a matching system that takes advantages of the third dimension present in the range images and casting this in the framework of an existing scale and rotational invariance recognition technology: SIFT. By exploring the statistical representations of the local variation, it is possible to represent and match range images of human faces. This can be achieved by extracting unique mathematical keys known as feature descriptors, from the various automatically generated stable keypoint locations of the range images, thereby capturing the local information of the distributions of the mixes of surface types and their orientations simultaneously. Keypoints are generated through scale-space approach, where the (x,y) location and the appropriate scale (sigma) are detected. In order to achieve invariance to in-plane viewpoint rotational changes, a consistent canonical orientation is assigned to each keypoint and the sampling patch is rotated to this canonical orientation. The mixes of surface types, derived using the shape index, and the image gradient orientations are extracted from each sampling patch by placing nine overlapping Gaussian sub-regions over the measurement aperture. Each of the nine regions is overlapped by one standard deviation in order to minimise the occurrence of spatial aliasing during the sampling stages and to provide a better continuity within the descriptor. Moreover, surface normals can be computed from each of the keypoint location, allowing the local 3D pose to be estimated and corrected within the feature descriptors since the orientations in which the images were captured are unknown a priori. As a result, the formulated feature descriptors have strong discriminative power and are stable to rotational changes